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Optimization often receives disproportionate attention in quantitative research. This article argues that signal stability and interpretability are often more important than marginal performance improvements.
We explore how unstable signals can lead to misleading backtests and poor generalization. The discussion highlights techniques for evaluating signal robustness without excessive tuning.
By prioritizing stability, researchers can develop frameworks that are easier to understand, explain, and iterate upon responsibly.
